Download Projections of precipitation, air temperature and potential

Vol. 10(1), pp. 18-33, January, 2016
DOI: 10.5897/AJEST2015.1997
Article Number: 5A55C7956454
ISSN 1996-0786
Copyright © 2016
Author(s) retain the copyright of this article
http://www.academicjournals.org/AJEST
African Journal of Environmental Science and
Technology
Full Length Research Paper
Projections of precipitation, air temperature and
potential evapotranspiration in Rwanda under changing
climate conditions
Mohammed Haggag1*, Jean Claude Kalisa2 and Ahmad Wagdy Abdeldayem1
1
Department of Irrigation and Hydraulics, Faculty of Engineering, Cairo University, Orman, Dokki, Giza, P. O. Box
12613, Egypt.
2
Ngali Energy Limited, Nyarutarama (P.O. Box 7189), Kigali, Rwanda.
Received 21 August 2015; Accepted 04 November, 2015
Precipitation and air temperature records from 6 sites in Rwanda in the period from 1964 to 2010 are
used for past/present climate assessment. Future climate projections (2010-2099) based on 3 general
circulation models and 2 emission scenarios (A2 and B1) are used for climate projections. Precipitation,
air temperature, and potential evapotranspiration based on ccma_cgcm3_1.1; miroc3_2medres and
mpi_echam5.1 models are used in the analysis. Observed air temperatures suggested warming pattern
over the past 40 years at an average of 0.35°C per decade. Rainfall records show no significant trend in
the considered period. The potential evapotranspiration has an increasing trend and exceeds the
precipitation in the months of June to September. Climate projections indicate trend towards a warmer
and wetter climate. Increases in mean temperature are projected under all models and scenarios, while
all models also indicate increases in annual rainfall totals. Despite of the projected wetter climate, the
st
increase in potential evapotranspiration will overrule during the 21 century resulting in deficit in water
availability for the rainfed agriculture. Deficit periods in which potential evapotranspiration exceeds
precipitation will be extended to 10 months at some parts in the country instead of 4 months at present.
Key words: Climate
evapotranspiration.
change,
Rwanda,
hillside
irrigation,
air
temperature,
precipitation,
potential
INTRODUCTION
Climate change can be defined as the change in
statistical properties of the climate system over extended
periods of time regardless of the causes of this change.
Earth is warming up, and there is now overwhelming
scientific consensus that warming is happening and it is
human-induced
(Finnis
et
al.,
2015).
The
Intergovernmental Panel on Climate Change (IPCC,
2014) provides startling details of the devastating impact
climate change could have on development in different
sectors namely agriculture, water resources, human
health, ecosystem and biodiversity. Understanding the
spatial and temporal variation of climate within a zone or
region, and their relationship with other factors is
important in activities related to climate change and the
*Corresponding author. E-mail: [email protected].
Author(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution
License 4.0 International License
Haggag and Kalisa
19
Figure 1. Location of Rwanda in the center of Africa.
management of the natural resources, such as
environmental planning, land use planning, water
resources planning, agriculture and irrigation.
The countries of Eastern Africa are prone to extreme
climatic events such as droughts and floods. In the past,
these events have had severe negative impacts on key
socioeconomic sectors of the economies of most
countries in the region. In the late seventies and eighties,
droughts caused widespread famine and economic
hardships in many countries of the region. There is
evidence that future climate change may lead to a
change in the frequency or severity of such extreme
weather events, potentially worsening these impacts. In
addition, future climate change will lead to increases in
average mean temperature, and changes in annual and
seasonal rainfall. These will have potentially important
effects across all economic and social sectors, possibly
affecting agricultural production, health status, water
availability, energy use, biodiversity and ecosystem
services.
Changing climate patterns will have important
implications for water availability in Africa. According to
the IPCC (2014), by 2030 an additional of 75 to 250
million people in Africa are projected to be exposed to an
increased water stress due to climate change. The most
important pressure on renewable water resources is
linked to the agricultural sector in which irrigation
represent the maximum demands (Valipour, 2014a, b).
The population growth triggers the need for additional
resources to satisfy the increasing food and living
requirements. Valipour (2015a) estimated the ratio of
area equipped for irrigated to cultivated area in Africa in
2035 and 2060. The results show an increasing trend of
irrigated areas in different parts of Africa from 0.3 to
49.5% and 16.5 to 83.2% from 2011 to 2035 and 2060,
respectively. Rao et al. (2015) shows that the areas
suitable for agriculture, the length of growing seasons
and the potential yield of food staples are all projected to
decline with some African countries could see agricultural
yields decrease by 50% by 2050 and crop net revenues
could fall by as much as 90% by 2100.
This paper focus on investigating past/present climate
conditions and future climate projections in some
potential hillside irrigation sites in Rwanda. Rwanda is a
landlocked country located in central east Africa (Figure
1). It is surrounded by four neighboring countries:
Uganda to the north, Tanzania to the east, Burundi to the
south and the Democratic Republic of Congo to the west.
Rwanda is highly dependent on natural resources and
agricultural growth is critical for pro-poor growth. Climate
change is likely to add to existing pressures including
change in precipitation pattern, increased temperature,
etc.
Results of regional historical climate trends from
stations in Rwanda, Kenya and Tanzania, indicate that
th
during the 20 century, the trend of daily maximum
temperature is not significantly different from zero.
However, daily minimum temperature suggests an
accelerating temperature rise (Christy et al., 2009). A
further study looking at day and night temperatures
20
Afr. J. Environ. Sci. Technol.
concluded that the northern part of East Africa region
generally indicated nighttime warming and daytime
cooling in recent years. The trend patterns were,
however, reversed at coastal and lake areas.
There were thus large geographical and temporal
variations in the observed trends, with some neighboring
locations at times indicating opposite trends. Air masses
arising from the seasonal shift of the intertropical
convergence zone that are transported between the anticyclones of the northern and southern hemispheres
shape Rwanda’s climate (Henninger, 2013). Henninger
(2009) analyzed the observed air temperature at Kigali
using 3 meteorological stations maintained by the by the
“Service Meteo du Rwanda” in the period from 1971 to
2008. The data indicated an increasing annual mean
temperature of 2.6°C for a period of nearly 40 years.
Mainly, for the last 10 years from 1998 to 2008, a
warming in Kigali is evident and could be attributed to
global warming and to the ongoing urbanization. Schreck
th
and Semazzi (2004) showed that during the 20 century,
the region of eastern Africa has been experienced an
intensifying dipole rainfall pattern on the decadal timescale. The dipole is characterized by increasing rainfall
over the northern sector and declining amounts over the
southern sector. East Africa has suffered both excessive
and deficient rainfall in recent years (Webster et al.,
1999). In particular, the frequency of anomalously strong
rainfall causing floods has increased. Shongwe van
Oldenborgh and Aalst (2009) report that their analysis of
data from the international Disaster Database (EM-DAT
shows that there has been an increase in the number of
reported hydrometeorological disasters in the region,
from an average of less than 3 events per year in the
1980s to over 7 events per year in the 1990s and 10
events per year from 2000 to 2006, with a particular
increase in floods.
Agriculture is the backbone of Rwanda’s economy,
accounting for about 43% of GDP (CIA, 2010), and 63%
of foreign exchange earnings. It also provides 90% of the
country’s food needs and 80% of the country’s labor force
is engaged in agriculture. Total arable land in Rwanda is
slightly above 1.5 million ha, 90% of which is found on
hillsides. Land husbandry water harvesting and hillside
irrigation, is a mean for improvement of rural productivity
and diversification of market-oriented agricultural
commodities. Considering the agriculture input to
Rwandan GDP and the percentage of Hillside arable
land, water harvesting and hillside irrigation is one of the
key government tools for poverty alleviation in Rwanda
and makes the investigation of the potential impacts of
climate change on this sector is a prerequisite.
Climate change due to global warming might impact the
agriculture development plans in this small country by
imposing increased air temperature, seasonal variation of
precipitation, flooding and drought, soil erosion, spread of
water borne diseases (e.g. Malaria), etc. Valipour (2015b)
analyzed the status of irrigated and rained agriculture at
wide scale, it is found that major portion of the cultivated
areas are not suitable for rainfed agriculture because of
climate changes and other meteorological conditions.
Potential uncertain effects from warming still need to be
investigated for regional agricultural adaptation to climate
change (Valibour, 2015c).
Drought and floods are other direct impacts of warming;
in 2002 heavy rains caused by unusually high
temperatures over the Indian Ocean killed more than 112
people in East Africa. Floods and mudslides forced tens
of thousands of people to leave their homes in the sub
region with Rwanda suffered the heaviest toll (Douglas et
al., 2008). Global climatic change is expected to increase
the incidence of vector-borne diseases, especially
malaria (Tanser et al., 2003). Some studies assessed the
contribution of climate to a malaria epidemic in Rwanda,
proving the correlation between malaria incidence and
climatic variables (Loevinsohn, 1994; Hay et al., 2002).
In this paper, 3 climatological parameters are assessed
(precipitation, surface air temperature and potential
evapotranspiration). Such parameters are the most
important to assess the impact of climate change on
hydrological conditions, water availability, and agriculture
productivity. Six sites with potential hillside irrigation are
considered for detailed analysis and all are located within
the Nile basin part in Rwanda that represent 67% of the
country total area and provide 90% of the country’s water
resources.
Study area
Rwanda is located in Central Africa between latitude 1°4’
and 2°51’ South of the equator and between longitude
28°45’ and 31°15 East (Figure 1). Its total area is 26.338
2
2
km with land occupying 24,666 km ; while the rest
occupied by water. Rwanda has mountainous and sloppy
landscape with altitude varying from 900 to 4,507 m over
a territory of about 400 km wide. Rwanda has dense
2
hydrological network with a stream density of 2 km/km . It
is split up into 2 basins by a water divide line called
“Congo-Nile Ridge”: at the east of divide is the Nile Basin
covering 67% of the national territory and at the west is
the Congo Basin (Figure 2). The former comprises many
small lakes and drains 90% of national waters through
two major rivers, Nyabarongo and Akagera. The Congo
Basin, covering 33% of Rwanda, drains 10% of national
water resources towards Lake Kivu (102,800 ha on
Rwanda side). Average surface water flow rates
3
measured at major hydrological stations are 78 m /s for
3
Nyabarongo at Kigali; 100 m /s for Nyabarongo at
3
Kanzenze, 232 m /s for Akagera at Rusumo, and 256
3
m /s for Akagera at Kagitumba (MINIRENA, 2012).
Because Rwanda lies near the equator, Rwandan
territory belongs to the inner or moist tropics. The
regional climate in Rwanda can be classified following
Köppen and Geiger climate classification (Prioul, 1981).
Haggag and Kalisa
21
Figure 2. Rwanda’s water resources network in 2009 (MINIRENA, 2009).
The hilly nature of Rwanda controls the windward and lee
locations clearly influencing the country’s rainfall and how
it is distributed. The amount of rainfall varies between
1000 and 1500 mm (Bamusananire et al., 2006). An
analysis of rainwater potential revealed that at the
3
national level Rwanda receives about 28 km of annual
3
rainfall. About 4.3 km are generated as runoff water, 9.5
3
3
km are lost to evaporation, 5.3 km are transpired by all
3
3
vegetation, and 4.8 km for other uses while 4.3 km
percolate into the groundwater system (Malesu et al.,
2010). In 2000 the estimated total annual water
3
consumption was 150 million m . Agriculture accounted
for 68%, domestic needs 24%, and industry 8% of total
consumption. Although located only two degrees south of
the Equator, Rwanda's high elevation makes the climate
temperate. The average daily temperature near Lake
Kivu, at an altitude of 1463 m is 23°C. During the two
rainy
seasons
(February-May
and
SeptemberDecember), heavy downpours occur almost daily,
alternating with sunny weather. Annual rainfall averages
800 mm but is generally heavier in the western and northwestern mountains than in the eastern savannas.
Rwanda possesses abundant water resources,
however, these resources are not evenly distributed, and
the quantity and quality may not be adequate. The
conditions are critical in the hilly agricultural land,
particularly in the eastern regions where rainfall is scarce.
According to the Food and Agriculture Organization
(FAO, 2007), Rwanda’s irrigation potential is 165,000 ha
of which only 7000 ha is currently irrigated that is only 4%
of the country’s potential (LWH03, 2008). The prevalent
method of irrigation is flood irrigation system, which is
used in the marsh lands in the country. Moreover, the
eastern province of the country with crops that are not
drought resistant could not be cultivated without
supplementary irrigation. Such lack of water in the dry
seasons for the hillside agriculture has drastically
affected productivity of the perennial agricultural crops
especially pineapple and plantain on which livelihoods of
the country and district community depends.
MATERIALS AND METHODS
Potential hillside irrigation sites
Rwandan irrigation potential indicates that the country has a
potential of about 589,713 ha, taking into consideration runoff, river,
lake, groundwater, small reservoirs and marshland domains
(Malesu et al., 2010). Following the keenness of the Government of
Rwanda to transform the irrigation potential into reality in order to
achieve food security, a number of interventions have to be initiated
to develop short, medium, and long-term strategic irrigation plans.
The potential hillside irrigation sites in Rwanda that subject to this
study are shown in Figure 3. Sites are Gastibo 8 and 32, Kayonza
15, and Bugesera 3 and 4, and Nyanza 23 located in Gastibo,
22
Afr. J. Environ. Sci. Technol.
Figure 3. Map of the potential hillside irrigation sites in Rwanda (Source: LWH32; 2008).
Kayonza, Bugesera and Nyanza districts, respectively, and all are
located in the eastern part of the country that belongs to the Nile
Basin.
Observation datasets and gap filling
Because of the history of the civil war and genocidein Rwanda,
hydrometeorological records are incomplete with extended periods
of data gaps. Kigali Airport meteorological station provides almost
complete and continuous record from 1964 to 2010 and it is
selected to be the reference rain gauge in filling missing data at
other rainfall gauges in the study area. Precipitation and air
temperature records from six potential hillside irrigation sites
(Nyanza 23, Bugesera 03, Bugesera 04, Kayonza 15, Gatsibo 32
and Gatsibo 08) are collected and checked against gaps and other
types of noise. The period from 1964 to 2010 is decided to be the
reference for analysis of observations because of the availability of
the full record at Kigali airport station.
Global climate datasets and emission scenarios
Climate projections are obtained from 3 GCMs and 2 emissions
scenarios (A2 and B1). Each scenario presents different
atmospheric concentrations of future greenhouse gases. While A2
does not represent the highest CO2 emissions (at least through
2100) of the SRES scenarios (IPCC, 2007), 21st century emissions
to date appear to be above this projection (Raupach et al., 2007).
A2 emission scenario used to be the highest emission scenario for
which most modelling groups have completed simulations. B1
emission scenario generally represents the best case of the SRES
scenarios through the 21st century (IPCC, 2001). All data are
obtained from the World Climate Research Programme’s (WCRP’s)
Coupled Model Inter-comparison (CMIP3) multi-model dataset
(Meehl et al., 2007). These data were downscaled as described by
Maurer et al. (2010) using the bias-correction/spatial downscaling
method (Wood et al., 2004) to a 0.5° grid, based on 1950-1999
gridded observations of Adam and Lettenmaier (Yang et al., 2005).
Temperature and precipitation data corresponding to A2 and B1
for 3 GCMs namely Canadian ccma_cgcm3_1.1; Japanese
miroc3_2medres and German mpi_echam5.1models are used for
future projection analysis. Delta approach is used to apply or
transfer changes relative to twentieth century (20 cm3) baseline on
site level. As an alternative of calculating a single percentage
change for precipitation or a single addition/subtraction value for
temperature for the whole period, we used periods (2010-2039,
2040-2069, and 2070-2099) relative to the reference period (19601990). The change in every month from the A2 or B1 scenarios
relative to the same month in the run of the twentieth century (20
cm3) is multiplied or added to the observed time series of
precipitation or temperature. For instance having 5% as the
percentage change for January 2010 to 2039 in the A2 scenario
relative to January 1960 to 1990 of the run of the twentieth century,
Haggag and Kalisa
mean that the value of precipitation for January 2010 to 2039 will be
1.05 the observed value for January 1960 to 1990.
Regression analysis
A linear relationship is established between Kigali Airport rainfall
station (reference station) and other rainfall stations. A linear
equation is established to calculate the least squares fit for a line,
Equation (1):
(1)
Where m is the slope and b is the intercept.
Once the equation representing the best fit between the
reference station and the station of interest with missing data is
established, the same equation is then used to generate the
missing data.
The generated data are checked and verified usingPearson
correlation coefficient (r) and coefficient of determination (R2) in
addition to Z-test.Pearson correlation coefficient, like the
covariance, is the measure of the extent to which two measured
variables ’’vary together’’. Unlike the covariance, Pearson
correlation coefficient is scaled so that its value is independent of
the units in which the two measured values are expressed. The
Formula for correlation coefficient if expressed by Equation (2):
(2)
Where: x = observed value, y = predicted value,
Precipitation disaggregation
Based on the fact that the area of interest of this study is relatively
small resulting in proximity of rainfall stations to each other, we
assumed that the daily rainfall distribution at all rainfall gauges will
be the same. Based on that, we constructed synthesis daily rainfall
time series at any station by disaggregating its total monthly rainfall
at such station based on the daily rainfall record observed at Kigali.
Mathematically, this can be expressed by assuming the daily rainfall
depth at Kigali station is , i with day order ranges from 1 to 31 in
a specific month. The total monthly-accumulated rainfall depth in
this month at Kigali is given by Equation (4):
(4)
By assuming that the daily rainfall depth at any other station is
,
where i refers to day order, the total monthly-accumulated rainfall
depth in a certain month at this station is expressed by Equation
(5), and the daily rainfall at
Generated data verification
= the average of
observed value, and = the average of predicted value.
Z test is a statistical procedure used to test the hypotheses
concerning the mean in a single population with a known variance.
A population mean is greater than (>), less than (<), or not equal (≠)
to the value stated in a null hypothesis. The alternative hypothesis
determines which tail of a sampling distribution to place the level of
significance. Z is considered significant if the difference is more
than roughly two standard deviations above or below zero (or more
precisely, |Z| > 1.96)” Collins and Morris (2008). The Z measure is
calculated as:
Z = (x - m) / SE
Where x is the mean sample to be standardized, m is the
population mean and SE is the standard error of the mean, that is,
SE = s / SQRT (n) where s is the population standard deviation and
n is the sample size. The z value is then looked up in a z-table. A
negative z value means it is below the population mean. For
comparing the mean of two variables, Equation (3) is used for data
similarity condition:
(3)
can be expressed by Equation (6).
(5)
(6)
Potential evapotranspiration calculation
Precise estimation of evapotranspiration is obtained using
lysimeters or imaging techniques at relatively high cost. Instead, it is
possible to calculate the actual evapotranspiration using crop
coefficients and potential evapotranspiration. The most commonly
applied technique for the estimation of potential evapotranspiration,
in various regions of the world, is the FAO Penman-Monteith
method (Allen et al., 1998), however this method needs many
meteorological parameters to estimate the potential crop
evapotranspiration. Due to the scarcity of weather data in Rwanda,
we could not use the FAO Penman-Monteith method, hence other
experimental methods with limited weather data requirements could
be used instead. Valipour (2014c, d, e; 2015d, e) provided an
inventory for different evapotranspiration methods applicable for
regions with limited weather data availability. The different methods
include mass transfer, radiation, temperature, and pan evaporationbased models.
In this study, Thornthwaite method (Thornthwaite, 1948) which is
a temperature based method and gives estimates of potential
evapotranspiration (PET) on a monthly basis is used (Equation 7).
(7)
Where: PET: Monthly potential evapotranspiration in mm, La:
Monthly Adjustment factor for the number of hours of daylight,
related to the latitude of the place,
(°C)
Where: n1 = sample 1 size; n2 = sample 2 size; = sample mean;
μ0 = hypothesized population mean, μ1 = population 1 mean; μ2 =
population 2 mean; σ = population standard deviation; and
σ2 = population variance.
23
: mean monthly air temperature
, and
, a is an empirical constant given by Equation (8).
(8)
24
Afr. J. Environ. Sci. Technol.
Table 1. Summary of gap filling and data generation (R2, R and Z test).
Station name
Butare (Nyanza23)
Ngarama(Gastibo8)
Bicumbi(Kayonza15)
Nyamata(Bugesera4)
Karama(Bugesera3)
Kiziguro(Gastibo32)
Kigali airport
Station available data relationship with the base station data
2
Equation
R
*
**
y = 0.99x + 19.23
0.62
y = 0.971x + 5.537
0.60
y = 0.755x + 17.44
0.61
y = 0.840x + 14.82
0.60
y = 0.755x + 17.44
0.61
y = 0.840x + 14.82
0.60
-
Comparison with TRMM data
Z
R
-1.66
0.85
-1.42
0.96
-1.67
0.96
-0.72
0.96
-0.15
0.93
-0.65
0.99
-0.30
0.95
*y is the missing value at a given rainfall station; **x is the corresponding value at Kigali.
Figure 4. Average monthly precipitation at LWH sites in Nile basin and Kigali station from 1971-2010.
For the past PET calculation, observed air temperature at
Kigarama, Rubona and Karama meteorological stations were used
as the temperature input, while for future projection of PET, GCM
temperature fields at the same locations as GCMs are used.
For verification purposes, CROPWAT 8.0 model for PET
estimation, which is based on Penman equation is used to get
corresponding PET values. Since some meteorological parameters
needed for Penman equation are not available in Rwandan
meteorological data office, instead we used FAO database which is
built-in CROPWAT8.0.
Observed precipitation
coefficient and Z test. The correlation with TRMM varies
from R= 0.85 to 0.99 at Butare and Kiziguro stations,
respectively, while Z test is varying from |Z|= 0.15 to 1.67
at the same stations. This implies that there’s no
significant difference between the two data sources.
TRMM data proves to be usable when observed rainfall
data are unavailable. The lowest correlation with TRMM
data is found at Butare station, nearest to Congo Basin,
and the highest correlation at Kizuguro station, farthest
from the Congo Basin. Figure 4 shows the annual trend
of rainfall season in Rwanda based on the average
monthly rainfall record from 1964 to 2010. The rainfall
season can be subdivided into four parts:
Table 1 presents the linear best fit relationships
established between Kigali rain gauge and other rain
gauges. Filled and generated data were compared to
TRMM rainfall observations using Pearson correlation
1. Long rainy season (March-May) with peak in April
when the rain is heavy and persistent. Average monthly
rainfall is between 110 and 160 mm/month, except for
Nyanza site where it reaches 190 mm/month. April is the
RESULTS AND DISCUSSION
Haggag and Kalisa
25
Figure 5. Annual average observed rainfall, for six rainfall stations from 1964 to 2010.
month with the highest monthly rainfall as recorded
during the last 47 years with 15% of average annual
precipitations while the driest month is July with 2% of
average annual precipitation.
2. Long dry season (June to September); with average
monthly rainfall between 10 and 50 mm/month, it may
reach even zero in July which is the driest month followed
by June.
3. Short rainy period (October to November) with average
monthly rainfall between 80 and 125 mm for all sites,
excluding Nyanza where the average monthly rainfall
reaches 145 mm.
4. Short dry season (Dec. to Feb.) with an average
monthly rainfall varying between 80 and 95 mm.
Figure 5 illustrates variations of annual average rainfall
from 1964 to 2010 at the six sites. All station oscillates
between 700 and 1300 mm per year, excluding Butare
station with higher annual rainfall, fluctuating between
900 and 1520 mm for the 47 years, with highest values
found in 1998 and 2001.
GCMs precipitation
There is a trend of precipitation increase in the range of 1
to 29% corresponding to 2010-2039 and 2070-2099
duration under all cases. Figure 6 shows how the
precipitation will change until 2099. Miroc3 A2 SRES has
the highest rainfall change varying from 6 to 29%
increase, while B1 is varying from 3 to 15%. Cgcm3 A2
SRES has shown a precipitation increment varying from
4 to 26%, while according to B1 SRES the precipitation
will vary from 7 to 14%. Echam5.1 A2 SRES gives lowest
precipitation change (-1 to 12%) at the end of the century,
while corresponding B1 scenario precipitation will vary
from -1 to 7%.
The importance of mitigation measures is obvious,
where Miroc3 is portraying a precipitation increase
difference for A2 above B1 varying from 3 to 14%, while
ranges of 0 to 12% and 0 to 6% differences are recorded
for Cgcm3 and Echam5.1, respectively. Figure 7a is also
demonstrating how mitigations measures will stabilize the
precipitation since there is no much change in the
average precipitation change factor along the whole year
while scenario A2 demonstrates a lot of precipitation
fluctuation from June to September (Figure 7b).
With respect to the annual average precipitation,
Echam 5.1 has the lowest projections with annual
precipitation vary from 942 to 1640 at Bugesera and
Nyanza. Miroc3 predics the highest annual precipitation
and vary from 784 to 1915 mm/year at Bugesera and
3
Nyanza, followed by Cgcm that vary from 925 to 1832
mm at Kayonza and Nyanza (Figure 8).
26
Afr. J. Environ. Sci. Technol.
Figure 6. Projection for average 30 years precipitation factor (P/Po) for all models for SRES A2 and B1. (Po is the
base line precipitation, 1980 to 2009).
Figure 7. Predicted monthly average precipitation increment for Cgcm3 A2 & B1.
Observed air temperature
Figure 9 shows the average air temperature, lower values
are observed at higher altitudes with average values
between 15 and 17°C. Moderate temperatures are
observed at intermediate altitudes with average values
between 19 and 21°C. In the lowlands (east and
southwest), temperatures are higher and can go beyond
Haggag and Kalisa
27
Figure 8. Annual average precipitation from 2010 to 2099 by Miroc, Cgcm and Echam GCMs for SRES A2 and B.
Figure 9. Mean monthly temperature for the last forty years for Karama, Kigali, Rubona and Gahororo meteological
stations.
30°C in February, July, and August. During the entire
year, there is no significant temperature change for the
four sites except for the months of June to September,
with a variation between 1 and 1.5°C with the highest
monthly average temperatures occur in August and the
lowest monthly average temperatures occur in
November.
Analysis of observed air temperature records show that
28
Afr. J. Environ. Sci. Technol.
Figure 10. The Rubona, Karama, Gahororo stations temperature increment relative to 1964-1975 baseline period.
Figure 11. Kigali, Rubona, Karama and Gahororo annual average temperature for the last 40 years.
over the last 47 years there is an increase in the average
air temperature. Using the period from 1964 to 1975 as
baseline, it is found that since 1964 to 2010, in Karama,
Gahororo and Rubona meteorological
stations;
temperature has increased by 1.51, 1.70 and 1.32°C,
respectively, as shown in Figure 10. Maximum
temperature increase per decade is found to be always
increasing with time progressing, the last period from
2002-2010 witnessed the highest temperature increase at
all sites with maximum air temperature increase of 1.7°C
at Kigarama site. The increasing temperature trend in
Figure 11 shows variation of at least 3°C in the observed
air temperature record at different sites confirming the
incremental factor calculated and presented in Figure 10.
Air temperature projections from GCMs
Subsequent to the analysis of the air temperature
projections, it is found that temperature increase at all
sites vary between 0.75 and 4.51°C corresponding to
2010-2039, and 2070-2099 time steps under Miroc3 B1
and Echam5.1 A2, respectively (Figure 12). Air
temperatures are analyzed in a batch of thirty years’ time
steps relative to the base period from 1960 to 1990. The
highest temperature increase is projected from Echam A2
and varies between 1.08 and 4.51°C at Gastibo and
Nyanza sites, respectively, while under Echam B1 the
temperature increase varies between 0.8 to 2.92°C.
According to Cgcm A2, air temperature increase
increment varies from 1.21 and 3.57°C, while under
Cgcm B1, temperature air increase increment fluctuates
between 1.11 and 2.14°C at the same sites. Miroc results
in the lowest temperature increase increment, whereby
A2 scenario projects an increase between 1.11 and
3.28°C and B1 projects a temperature increase increment
that vary from 0.75 to 2.14°C from 2010 to 2099.
Furthermore, there’s not much change in air
Haggag and Kalisa
29
Figure 12. 30 years average temperature increase relative to 20th century base line.
Table 2. GCMs predicted average temperature increment range for all sites.
GCM name
mpi_echam5.1
ccma_cgcm3_1.1
miroc3_2medres.1
temperature during different months of the year, with the
highest average temperature in August and the lowest in
November, varying between 19.5 and 28°C, with a
maximum annual temperature variation of 3°C, (not
shown). Table 2 summarizes the outcomes of the
projected air temperature analysis from the 3 GCMs. All
st
models agreed on a warming trend during the 21
century. The range of warming varies from 0.75 to 4.5°C.
PET calculation using observed data
Similar to the results of the air temperature analysis,
there is no doubt that PET is increasing (Figure 13). The
4 investigated sites show an apparent increase in PET in
the period from 1964 to 2010, although the increase
increments differ from one site to another. Using PET
data from 1964-1980 as baseline period, the following
changes in PET are established. PET increased by 5, 4,
3 and 3% at Kigarama, Karama, Kigali and Rubona sites,
respectively. The PET increase increments, from 1991 to
2000 for all sites are 1%. From 2001-10 PET has
increased by 2, 4, 3.7 and 3% for Kigarama, Karama,
Kigali and Rubona sites, respectively, and this is the time
period with the highest increment at all sites. Finally, it is
found that during the last four decades PET has
o
Predicted increment range ( C)
0.8 to 4.51
1.11to 3.57
0.75 to 3.28
increased by 8, 10 and 7% at Kigarama, Karama and
Rubona sites, respectively. Since 1971, the highest PET
monthly average increment is 19% observed between
2001 and 2010 at Karama station, followed by 15% for
the same time period and all take place in the month of
July (Figure 14). As shown in Figure 15, from June to end
August there is no enough precipitation to contribute to
runoff, since these months correspond to the long dry
season in Rwanda, while the trend shows enough
precipitation contributing to the runoff during the rest of
the year.
PET calculation from GCMs projections
Figure 16 shows that PET projections at all sites have
increasing trend by 3 and 55% corresponding to 20102039 and 2070-2099 periods under Echam 5.1 B1&A2
SRES 2, respectively. All models predicted high values at
Bugesera site, while the lowest values are found at
Nyanza site. As Thornthwaite formula is an air
temperature based method, the PET trend shows the
same behavior as that of the air temperature. Echam5.1
A2 has the highest prediction varying from 6 to 55%
increase while B1 is varying from 3 to 28%. Cgcm3 A2
has shown a PET increment varying from 5 to 34% while
30
Afr. J. Environ. Sci. Technol.
Figure 13. 30 years record for monthly average PET for Kigarama, Karama, Rubona and Kigali station for 19711910.
Figure 14. Monthly average PET increment factor for Karama, Rubona, Kigarama and Kigali
sites relative to 1964-80 observations.
Figure 15. Sites average monthly precipitation versus monthly PET.
Haggag and Kalisa
31
Figure 16. 30 years PET average increment factor relative to the 20 cm 3 base line.
Figure 17. Plot of precipitation versus PET at Bugesera site based on EchamA2 projections.
according to B1 the PET will vary from 4 to 16% Miroc3
A2 has predicted a PET varying from 4 to 34% at the end
of the century, while according to B1 scenario the PET
will vary between 4 to 24% at the end of the 21st century.
Figure 17 provides a comparison between projected
precipitation and projected PET at a sample site. It is
noticed that both Cgcm and Miroc predicted 4 months
with water deficit at Bugesera while Echam 5.1 predicted
more severe conditions in which water deficit last for 10
months with precipitation exceeds PET only in April and
November. Although the analysis has shown that
precipitation will increase by 8, 10 and 28% in 2039, 2069
and 2099, respectively at Bugesera site, PET projections
indicate that there are at least 10 months that experience
water deficit in which the PET exceeds the precipitation.
This situation is noticed at different sites in the Nile part
of Rwanda which emphasize the impact of climate
change on the irrigation practice and need for adaptation
measures centered about the supplemental irrigation in
months that have PET exceeds precipitation.
Conclusions
In this study analysis of Rwanda past/present
meteorological observations as well as of climate
projections of identified global circulation models and
emission scenarios is carried out. The analysis is focused
on meteorological parameters that mostly affect the
hydrology system and the quantification of irrigation water
(surface air temperature, precipitation and potential
evapotranspiration). Precipitation and air temperature
records from six potential hillside irrigation sites located in
the Nile basin part of Rwanda are collected in the period
32
Afr. J. Environ. Sci. Technol.
from 1964 to 2010. Future climate projection for the
period from 2010-2099 are acquired from 3 CMIP3
general circulation models for 2 emission scenarios (A2
and B1). Temperature and precipitation data from the
Canadian ccma_cgcm3_1.1; Japanese miroc3_2medres
and German mpi_echam5.1models are used for future
projection analysis.
Observed average air temperatures suggested
warming pattern over the past 40 years at an average of
0.35°C per decade. Some observed precipitation records
from 1964 to 2010 illustrate a decrease from 1980’s till
2000. The computed potential evapotranspiration is
following the rising trend of the air temperature with
potential evapotranspiration exceeds the received
precipitation in the months of June, July, August and
September.
Analysis of climate projections for Rwanda’s through
the 21st century indicates a trend towards a warmer and
wetter climate conditions. Increases in mean air
temperature,
precipitation
and
potential
evapotranspiration are projected under all models and all
emissions scenarios. The increases in rainfall are
generally small relative to the inter-annual variability
currently experienced in Rwanda. The highest mean air
temperature increase at the end of the 21st century is
predicted at Nyanza with a value of 4.5°C corresponding
to Echam-A2. The highest increase in potential
evapotranspiration is projected at Bugesera with an
increase increment of 55% corresponding to Echam-A2.
The highest precipitation increase increment is projected
to be 29% at Kayonza corresponding to Miroc-A2.
Despite of the projected wetter climate conditions in
Rwanda, the increase in potential evapotranspiration will
over rule during the 21st century resulting in deficit in
water availability for the rainfed agriculture. Deficit
periods in which potential evapotranspiration exceeds
precipitation will be extended to 10 months at some parts
in the country instead of 4 months at present. This
situation is projected at different sites in Rwanda which
emphasize the impact of climate change on the current
irrigation practices. The Rwandan development plans are
urged to include adaptation and mitigation measures to
cope with potential climate change impacts and
meanwhile initiating further climate related investigations.
Conflict of Interests
The authors have not declared any conflict of interest.
ACKNOWLEDGMENTS
The authors acknowledge The Rwandan and Egyptian
Governments, for providing a master scholarship for the
second author, through the scientific cooperation
programs between Egypt and Nile Basin countries.
Acknowledgment are due to Dr. Ahmed Wagdy Abdel
Dayem at Cairo University for his encouragement,
guidance and support. We thank LWH staff, Rwanda
meteorological office staff, and REMA staff for providing
the meteorological observations.
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